scholarly journals Use of a Collar-Mounted Triaxial Accelerometer to Predict Speed and Gait in Dogs

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1262
Author(s):  
Samantha Bolton ◽  
Nick Cave ◽  
Naomi Cogger ◽  
G. R. Colborne
Keyword(s):  
Regression Models ◽  
High Speed ◽  
Video Data ◽  
Accelerometer Data ◽  
Triaxial Accelerometer ◽  
Inherent Limitation ◽  
Logistic Regression Models ◽  
Accelerometer Signal ◽  
Delta G ◽  
Selection Of

Accelerometry has been used to measure treatment efficacy in dogs with osteoarthritis, although interpretation is difficult. Simplification of the output into speed or gait categories could simplify interpretation. We aimed to determine whether collar-mounted accelerometry could estimate the speed and categorise dogs’ gait on a treadmill. Eight Huntaway dogs were fitted with a triaxial accelerometer and then recorded using high-speed video on a treadmill at a slow and fast walk, trot, and canter. The accelerometer data (delta-G) was aligned with the video data and records of the treadmill speed and gait. Mixed linear and logistic regression models that included delta-G and a term accounting for the dogs’ skeletal sizes were used to predict speed and gait, respectively, from the accelerometer signal. Gait could be categorised (pseudo-R2 = 0.87) into binary categories of walking and faster (trot or canter), but not into the separate faster gaits. The estimation of speed above 3 m/s was inaccurate, though it is not clear whether that inaccuracy was due to the sampling frequency of the particular device, or whether that is an inherent limitation of collar-mounted accelerometers in dogs. Thus, collar-mounted accelerometry can reliably categorise dogs’ gaits into two categories, but finer gait descriptions or speed estimates require individual dog modelling and validation. Nonetheless, this accelerometry method could improve the use of accelerometry to detect treatment effects in osteoarthritis by allowing the selection of periods of activity that are most affected by treatment.

scholarly journals Pedestrian Compliance at Signalized Intersections along Major Arterials

2021 ◽  
Vol 15 (1) ◽  
pp. 210-216
Author(s):  
Khaled Shaaban
Keyword(s):  
Regression Models ◽  
Middle Age ◽  
Compliance Rate ◽  
Signalized Intersections ◽  
Video Data ◽  
Logistic Regression Models ◽  
Several Variables ◽  
Pedestrian Crashes ◽  
Pedestrian Crossing ◽  
Multiple Intersections

Background: Pedestrian non-compliance at signalized crossings is unsafe and considered one of the causes of pedestrian crashes. The speed limit on most major urban roads is 60 km/hr or less. However, the speed on some urban roads is higher in some countries. In this case, the situation is more unsafe and increases the possibility of fatal injuries or fatalities in the case of a crash. Therefore, it is expected that the pedestrians will be more cautious on these roads. Aim: This study aims to explore pedestrian compliance at signalized intersections on major arterials with 80 km/hr speeds in Qatar. Methods: Video data were collected for pedestrian movements at multiple intersections. Results: The study reported a 68.1 percent compliance rate at the study locations. The results also revealed that 14.6 percent of the pedestrians crossed during the Flashing Don’t Walk interval and 17.3 percent crossed during the Steady Don’t Walk interval. These rates are considered high compared to other countries. Several variables that may influence pedestrians’ behavior were investigated. Binary and ordinal logistic regression models were developed to describe the pedestrian crossing behavior as a function of these variables. Conclusion: Male and middle-age pedestrians were more likely to cross during these two intervals. The analysis showed that female pedestrians, elder pedestrians, pedestrians crossing in groups, pedestrians waiting before crossing, and pedestrians crossing against a flow of other pedestrians are more likely to comply and cross during the Walk interval compared to other groups. Several solutions were proposed in the study to increase compliance rates.

Sensor-based Fall Risk Assessment – an Expert ‘to go’

2011 ◽  
Vol 50 (05) ◽  
pp. 420-426 ◽  
Author(s):  
A. Rehwald ◽  
K. H. Wolf ◽  
M. Gietzelt ◽  
G. Nemitz ◽  
H. Meyer zu Schwabedissen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Fall Risk ◽  
Regression Models ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Risk Models ◽  
Logistic Regression Models ◽  
Follow Up Study ◽  
One Year

SummaryBackground: Falls are a predominant problem in our aging society, often leading to severe somatic and psychological consequences, and having an incidence of about 30% in the group of persons aged 65 years or above. In order to identify persons at risk, many assessment tools and tests have been developed, but most of these have to be conducted in a supervised setting and are dependent on an expert rater.Objectives: The overall aim of our research work is to develop an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. The aims of our work for this paper are to derive a fall risk model based on sensor data that may potentially be measured during typical activities of daily life (aim #1), and to evaluate the resulting model with data from a one-year follow-up study (aim #2).Methods: A sample of n = 119 geriatric inpatients wore an accelerometer on the waist during a Timed ‘Up & Go’ test and a 20 m walk. Fifty patients were included in a one-year follow-up study, assessing fall events and scoring average physical activity at home in telephone interviews. The sensor data were processed to extract gait and dynamic balance parameters, from which four fall risk models – two classification trees and two logistic regression models – were computed: models CT#1 and SL#1 using accelerometer data only, models CT#2 and SL#2 including the physical activity score. The risk models were evaluated in a ten-times tenfold cross-validation procedure, calculating sensitivity (SENS), speci ficity (SPEC), positive and negative predictive values (PPV, NPV), classification accuracy, area under the curve (AUC) and the Brier score.Results: Both classification trees show a fair to good performance (models CT#1/ CT#2): SENS 74% / 58%, SPEC 96% / 82%, PPV 92% / 74%, NPV 77%/82%, accuracy 80% / 78%, AUC 0.83 / 0.87 and Brier scores 0.14 / 0.14. The logistic regression models (SL#1/ SL#2) perform worse: SENS 42% / 58%, SPEC 82% / 78%, PPV 62% / 65%, NPV 67% / 72%, accuracy 65% /70%, AUC 0.65 / 0.72 and Brier scores 0.23 / 0.21.Conclusions: Our results suggest that accelerometer data may be used to predict falls in an unsupervised setting. Furthermore, the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. These promising results have to be validated in a larger, long-term prospective trial.

scholarly journals What drives the acquisition behavior of agroholdings? Performance analysis of agricultural acquisition targets in Northwest Russia and Ukraine

2021 ◽  
pp. 1-22
Author(s):  
Igor Ostapchuk ◽  
Taras Gagalyuk ◽  
David Epshtein ◽  
Abusupyan Dibirov
Keyword(s):  
Capital Structure ◽  
Transition Economies ◽  
Regression Models ◽  
Selection Criteria ◽  
Target Selection ◽  
Farm Size ◽  
Logistic Regression Models ◽  
On Farm ◽  
Over Time ◽  
Selection Of

This article provides pioneering empirical evidence on the selection of acquisition targets by agroholdings in transition economies. We use panel data from Ukraine and Northwest Russia covering the years 2005-2016. Binomial logistic regression models were estimated to analyze the impacts of farm capital strength, financial performance and size on the farm’s likelihood of being acquired by an agroholding. Our results indicate that agroholding target selection considerations tend to change over time and have shifted from farm size to farm performance in both countries. However, agroholdings in both countries prefer to ‘grab lemons’, i.e. acquire poorly performing farms, but they differ with respect to target selection criteria. Agroholdings in Northwest Russia tend to focus on farm profitability, while their Ukrainian counterparts emphasize the capital structure of the target farms.

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

2019 ◽  
Vol 85 (6) ◽  
pp. 53-63 ◽  
Author(s):  
I. E. Vasil’ev ◽  
Yu. G. Matvienko ◽  
A. V. Pankov ◽  
A. G. Kalinin
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
High Speed ◽  
Early Stage ◽  
Video Data ◽  
High Speed Video ◽  
Damage Diagnostics ◽  
Subsurface Defect ◽  
Sensitive Coating ◽  
Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Selection of the Optimal Type of Bored-Injection Piles Strengthening of Weak Bases of High-Speed Railway Lines

2019 ◽  
pp. 40-44
Author(s):  
N.S. SOKOLOV ◽  
◽  
S.S. VIKTOROVA ◽  
I.P. FEDOSEEVA ◽  
G.M. SMIRNOVA ◽  
...  
Keyword(s):  
High Speed ◽  
High Speed Railway ◽  
Weak Bases ◽  
Selection Of ◽  
Optimal Type

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

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